Winsorizing or winsorization is the transformation of statistics by limiting extreme values in the statistical data to reduce the effect of possibly spurious outliers. It is named after the engineer-turned-biostatistician Charles P. Winsor (1895–1951). The effect is the same as clipping in signal processing.
The distribution of many statistics can be heavily influenced by outliers. A typical strategy is to set all outliers to a specified percentile of the data; for example, a 90% winsorization would see all data below the 5th percentile set to the 5th percentile, and data above the 95th percentile set to the 95th percentile.
Winsorized estimators are usually more robust to outliers than their more standard forms, although there are alternatives, such as trimming, that will achieve a similar effect.
Consider the data set consisting of:
{92, 19, 101, 58, 1053, 91, 26, 78, 10, 13, −40, 101, 86, 85, 15, 89, 89, 28, −5, 41} (N = 20, mean = 101.5)
The data below the 5th percentile lies between −40 and −5, while the data above the 95th percentile lies between 101 and 1053 (pertinent values shown in bold); accordingly, a 90% winsorization would result in the following:
{92, 19, 101, 58, 101, 91, 26, 78, 10, 13, −5, 101, 86, 85, 15, 89, 89, 28, −5, 41} (N = 20, mean = 55.65)
After winsorization the mean has dropped to nearly half its previous value, and is consequently more in line with the data it represents.
Python can winsorize data using SciPy library :
from scipy.stats.mstats import winsorize
winsorize([92, 19, 101, 58, 1053, 91, 26, 78, 10, 13, -40, 101, 86, 85, 15, 89, 89, 28, -5, 41], limits=[0.05, 0.05])
R can winsorize data using the DescTools package:
library(DescTools)
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